A communications receiver typically needs to know the response of the communications channel, i.e., the response of the link between a remote transmitter and the receiver. This is especially the case in wireless communications receivers, which generally must cope with interference, multipath scattering, and fading of the transmitted signal, in addition to the reduced signal strength that arises from the distance between the transmitter and receiver. In many systems, the receiver performs frequent estimates of the channel response, since the channel response can change over time.
In the Wideband Code-Division Multiple Access (W-CDMA) systems standardized by members of the 3rd-Generation Partnership Project (3GPP), for example, estimates of the channel response, hereinafter called “channel estimates,” are derived from instantaneous channel measurements, which may in turn be obtained from the received and despread version of the Common Pilot Channel (CPICH) to CPICH symbols that are known to have been transmitted by the remote base station (frequently referred to as a “Node B” in 3GPP documentation). As discussed in further detail below, channel response estimates are often improved by smoothing of these channel measurements, e.g., by filtering several measurements corresponding to multiple measurement times, across one or more slots.
The initial channel measurements used for calculating channel response estimates may be available for different update time intervals, such as a symbol or a slot. They may correspond to specific path delays, e.g. in a DS-CDMA-based system like WCDMA/HSPA, and/or to specific signal frequencies or frequency bins, e.g., to specific sub-carriers in an Orthogonal Frequency-Division Multiplexing (OFDM) signal, such as those used in the so-called Long-Term Evolution (LTE) systems standardized by 3GPP. It should be appreciated that the channel transfer response will generally vary with frequency as well as with time, for wideband systems. Accordingly, channel estimation techniques may need to account for time-variation, frequency-variation, or both, in various systems.
As WCDMA and LTE systems evolve, it becomes increasingly important to build advanced receiver structures for both the mobile terminals (known as “user equipment” or “UEs” in 3GPP terminology) as well as the base stations. Such advanced receivers may be characterized, for example, by an ability to cancel known interferers, such as control channels, or in some cases to cancel downlink data transmissions to other UEs or uplink transmissions from other UEs. A necessary ingredient in such receivers is good channel estimates. Without good channel estimates, the cancellation of interfering signals may degrade the resulting signal quality, instead of improving it.
The initial channel measurements are typically filtered, i.e., “smoothed,” in order to obtain improved channel estimates. This smoothing may be done by averaging the measurements, performing linear regression with the channel measurements as suggested above, or by using another suitable linear filter. One well-performing and conceptually simple approach is to apply linear regression to the initial channel estimates from the recent past and to model the fading channel coefficient trajectory with slope and offset parameters. A straight line approximation of a fading channel often results in an adequate approximation for channel estimation purposes. More elaborate approximations of the fading pattern could be done, but that requires more statistical information about the fading realization that may not be readily available. Any of these techniques can be regarded as applying a filter response to the channel measurements, the filter response having a particular time constant and filter bandwidth.
Motion of the receiver or transmitter affects the time-varying nature of the channel response, as changes in the receiver or transmitter positions affect at least the multipath and fading characteristics of the channel. This problem is especially pronounced for high-velocity situations, such as when the receiver is in or attached to a vehicle. A faster rate of change in the channel response means that shorter filter times, i.e., larger filter bandwidths, must be used to avoid excessive biasing of the resulting channel estimates. Accordingly, in some cases, the filter bandwidth and/or other filter parameters used to obtain channel estimates may depend on the maximum Doppler shift and/or the noise power level. This allows the smoothing filter to be adapted to the velocity of the receiver or transmitter, while also taking account of the noise power level in some implementations. It is then possible to use more filtering at low velocities and less filtering at high velocities, for example. It is similarly possible to use more filtering when the noise power level is high and less filtering when the noise power level is low.
Generally, the optimal filtering solution is a trade-off between noise suppression and the estimation bias incurred due to errors in channel tracking. Channel estimation techniques need additional improvements to approach the optimal filtering solution over the wide variety of signal conditions and speeds likely to be encountered by a typical receiver.